A/B testing is a method for comparing the effectiveness of several different variations of a web page. For example, an online clothing retailer that specializes in mens’ streetwear may want to examine whether a black or pink background results in more purchases from visitors to the site. Lets say that our online store is just a single web page, and we run this experiment by randomly showing one variation (pink background) of the page to half the visitors and the control background to the other half. After running the experiment for one week, we find that the pink background resulted in 40% purchase rate with 500 visitors while the black background resulted in a 30% purchase rate with 550 visitors. So which background is more effective at generating purchases from visitors to the online store. One way to examine this problem is by calculating confidence intervals of the conversion rates for each variation of the site. In the following R code, I construct a function which calculates the confidence intervals for the purchase rate of each site at a 80% significance level. In this example, the purchase rate for the pink background is significantly higher than the purchase rate for the black background.

site1 = c(.40, 500) # pink
site2 = c(.30, 550) # black
abtestfunc <- function(ad1, ad2){
sterror1 = sqrt( ad1[1] * (1-ad1[1]) / ad1[2] )
sterror2 = sqrt( ad2[1] * (1-ad2[1]) / ad2[2] )
minmax1 = c((ad1[1] - 1.28*sterror1) * 100, (ad1[1] + 1.28*sterror1) * 100)
minmax2 = c((ad2[1] - 1.28*sterror2) * 100, (ad2[1] + 1.28*sterror2) * 100)
print( round(minmax1,2) )
print( round(minmax2,2) )
}
abtestfunc(site1, site2)

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